PractiTest MCP. Manage QA data, runs, and requirements from chat.
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PractiTest manages your entire QA lifecycle—from project definition to final run results—using AI Agents. This MCP Server lets your client fetch project details, list requirements, create new test instances and runs, and track quality assurance metrics in real-time via natural conversation.
It makes tracking complex software validation processes as simple as asking a question.
What your AI agents can do
Create instance
Creates a new test instance within a specific PractiTest project.
Create run
Initiates and creates an entirely new test run in the specified PractiTest project.
Create test
Generates a brand-new, structured test case inside a given PractiTest project.
List or get detailed information for any project within PractiTest.
Fetch the specific details of a requirement, ensuring QA coverage against defined specifications.
List and retrieve granular data on test runs, providing an immediate status of recent validation cycles.
Programmatically create new tests, instances, or entire project runs using structured input.
List all existing test instances or individual tests within a specific project scope for auditing.
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PractiTest MCP Server: 11 Tools for Quality Assurance
These tools give your agent the ability to read, create, and update every core data point in PractiTest—from project listing down to individual test instances.
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Start using PractiTest on Vinkius019dd140create instance
Creates a new test instance within a specific PractiTest project.
019dd140create run
Initiates and creates an entirely new test run in the specified PractiTest project.
019dd140create test
Generates a brand-new, structured test case inside a given PractiTest project.
019dd140get project
Fetches all core details for one specific PractiTest project ID.
019dd140get requirement
Retrieves detailed information about a single requirement within a project.
019dd140get test
Gets the full details for one specific test case in PractiTest.
019dd140list instances
Retrieves a list of all current and past test instances within a project.
019dd140list projects
Lists every single PractiTest project the API token has access to.
019dd140list requirements
Lists all defined requirements within a specific PractiTest project scope.
019dd140list runs
Provides an overview and list of recently executed test runs for a project.
019dd140list tests
Lists all defined test cases within the scope of a PractiTest project.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 11 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Gathering QA metrics shouldn't feel like filling out an expense report.
Today, running a simple status check means logging into the project dashboard. You have to navigate to the 'Runs' tab, grab the run ID, then maybe open another tab to cross-reference that run against the core requirement document in a separate tool. Then you copy IDs and paste them into your report.
With this MCP server, you just ask: 'What is the status of our latest build for Feature X?' Your agent calls `list_runs` and pulls all necessary data—the test ID, the run status, the linked requirement details—and gives you one clean answer. No clicks needed.
PractiTest MCP Server: Manage Project Lifecycle in Chat
You don't have to manually create a new test or run after writing code and then switch contexts. You just tell the agent, 'Create a performance test for Login endpoint.' The agent executes `create_test` and sets up the environment using `list_instances`, all while maintaining your conversation flow.
It changes everything. Your entire QA workflow becomes one continuous conversation with an expert assistant—not a series of disjointed clicks across five different applications.
What you can do with this MCP connector
This server gives your AI client full read/write access to everything in your PractiTest workspace. Forget logging into dashboards and clicking through tabs just to gather data; you'll manage the whole quality assurance process right from chat.
Project Scope & Metadata Management:
To figure out what projects exist, you can use list_projects to get a list of every PractiTest project your API token has access to. If you need all the core details for one specific job, call get_project with just the project ID. For requirements, you'll first see everything by calling list_requirements within a certain project scope.
Then, if you need deep background on a single rule, use get_requirement to pull up detailed info about that requirement.
Test Definition and Audit:
You can list all defined test cases in a project using list_tests. If your agent needs the full rundown on just one specific test case, it pulls that data with get_test. To audit what's happening on the ground, you can call list_instances to get a list of all current and past test instances within a project.
You'll also use list_tests when auditing individual tests.
Creating New Assets & Running Checks:
Need to start something? The server handles asset creation for you. To generate a brand-new, structured test case inside a given PractiTest project, your agent uses create_test. If you need to kick off an entirely new round of checks, it'll call create_run, initiating and creating that whole test run in the specified project.
You can also programmatically set up fresh testing environments by using create_instance for a specific test instance within a given PractiTest project.
Tracking Live Results:
To see what's been done, you call list_runs, which gives an overview and list of recently executed test runs for the project. If you need to pull granular status on live testing cycles, your agent uses list_instances again. This lets you track all the quality assurance metrics in real time just by asking a question.
019dd140-b117-706e-a7e6-4757f6d2d36e 
How PractiTest MCP Works
- 1 Subscribe to the PractiTest integration on Vinkius.
- 2 Provide your API Token credentials to link the server to your account.
- 3 Your AI client can then query, analyze, and manage specific QA data sets using tools like
list_projectsorget_requirement.
The bottom line is: you give the agent access via an API token, and it handles all the complex calls to PractiTest for you.
Who Is PractiTest MCP For?
QA Engineers who are tired of switching between Jira, spreadsheets, and the test platform dashboard. Project Managers needing a single view of software quality status without leaving their chat interface. Developers validating feature requirements on the fly.
Uses list_tests and create_instance to automate test case generation and track execution results at scale.
Runs queries like get_project or list_runs to pull aggregated QA metrics and report on project health status immediately.
Calls tools like get_requirement during a sprint review to validate that code meets the exact, documented specification.
What Changes When You Connect
- Stop manually gathering test metrics. Use
list_runsto pull live QA data directly into your agent's context, eliminating spreadsheet exports and copy-pasting IDs. - Automate the audit trail by using
get_requirement. Your agent can fetch a specific requirement's details and cross-reference it with existing tests (get_test) instantly. - Build test suites on demand. Instead of logging into the platform to create new checks, use
create_testorcreate_instancevia simple commands. - See all your projects in one place. The
list_projectstool lets you quickly see what QA work exists across your entire portfolio without navigating complex navigation menus. - Track execution status live. By calling
list_instances, you get an immediate, current view of the test environment's state—critical for debugging.
Real-World Use Cases
Validating a new feature branch
A developer needs to validate Feature X. They ask their agent: 'What requirements apply to Feature X?' The agent calls get_requirement and then uses that ID to list all relevant tests via list_tests. Finally, it checks the status by calling list_runs, giving the dev a complete pass/fail report without leaving Slack.
Quickly assessing project scope
A PM needs an overview of all QA projects for quarterly reporting. Instead of checking multiple dashboards, they ask their agent to run list_projects. The agent returns a list, allowing the PM to then call get_project on the top three candidates.
Reproducing a failed test
A QA Engineer finds an intermittent bug. They ask the agent to check past runs for that project. The agent calls list_runs, identifies the problematic run ID, and then uses get_test on that specific test case to pull logs and context for debugging.
Onboarding a new feature set
A team is starting work on a massive module. They use the agent to first call list_requirements, ensuring every piece of documentation is accounted for. Then, they systematically call create_test for each requirement before even writing code.
The Tradeoffs
Treating QA data like a simple database query
Trying to ask the agent 'Give me all test results.' This is too vague. The system doesn't know if you mean runs, tests, or projects.
→
You need to narrow it down. First, call list_projects to select the project scope. Then, use list_runs to get the list of executions, and finally, target specific data with get_test.
Assuming all test cases are ready
Running a full validation without checking if the required test assets exist first. This leads to failed calls.
→
Always check scope first. Before creating anything, call list_requirements and then verify dependencies by calling list_tests for that project.
Mixing up creation vs retrieval
Telling the agent to 'update test X.' This is ambiguous. Does it mean update its definition, or its status?
→
Be explicit. If you want to change the status, use create_run. If you need to define a new check, use create_test.
When It Fits, When It Doesn't
Use this server if your primary pain point is context switching between multiple QA tools (Jira, test platforms, documentation). You need an AI agent to act as the 'API layer' for your existing PractiTest data.
Don't use it if:
1. Your only goal is simple document storage or messaging—use a dedicated communication tool instead.
2. Your QA process relies on manual human review of raw logs that cannot be represented by an API call—the server can't read what the platform UI displays.
3. You need to manage code deployment itself—this handles testing, not CI/CD pipeline execution.
This tool excels when you must enforce a structured process: list_projects -> get_requirement -> list_tests -> create_instance. It forces traceability into your workflow.
Common Questions About PractiTest MCP
How do I get a list of all PractiTest projects using the PractiTest MCP Server? +
You use the list_projects tool. This function returns every project ID that your API token has access to, giving you a starting point for any deep dive.
Can I create a new test case with PractiTest MCP Server? +
Yes, you use the create_test tool. You provide the necessary data (as JSON) and the agent handles generating and saving the test inside your target project.
What is the difference between list_runs and list_instances? +
The list_runs tool shows you a list of completed or ongoing execution cycles. The list_instances tool lists individual, specific test environments that were used during those runs.
Does PractiTest MCP Server help me track requirements? +
Yes. You use the get_requirement and list_requirements tools to fetch detailed specs, ensuring your tests are always mapped back to the original business needs.
What credentials are required when using tools like `get_project` or `list_projects`? +
You must provide a valid PractiTest API Token. This token authorizes your agent and determines the scope of data it can read or modify across your projects.
When calling `create_test`, what format should I use for the input data? +
The tool requires the test data as a JSON string. You must structure all parameters—like test name, project ID, and steps—into valid JSON to ensure the creation process works.
Using `list_tests`, how do I filter or specify which tests are included in the list? +
The list_tests function provides all available test objects for a given project. While it lists everything, you'll need to use your agent prompt to filter the results based on status or author.
If I encounter an error while using any PractiTest tool, what should I check first? +
First, verify that the project ID provided in the request is correct and active. If the ID is valid, the API response code will specify whether the issue is authentication-related or a data mismatch.
Can the AI Agent execute tests inside PractiTest? +
While the agent cannot run automated testing scripts directly in PractiTest, it can create Test Runs, log results into Instances, and manage the administrative side of QA efficiently.
Are custom fields supported when creating new tests? +
Yes! The AI agent formats API requests dynamically. If your workspace requires custom fields, simply instruct the agent on which attributes to include during the test creation.
Is there a limit on how many tests the agent can list at once? +
The agent adheres to PractiTest API pagination limits. By default, it returns a single page of results, but you can explicitly ask the AI to query a different page number or limit.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.